Utiliza este identificador para citar o vincular este elemento: http://hdl.handle.net/10553/35490
Títulos: Manifold embedding and semantic segmentation for Intraoperative guidance with hyperspectral brain imaging
Autores/as: Ravi, Daniele
Fabelo Gómez, Himar 
Marrero Callicó, Gustavo 
Yang, Guang-Zhong
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Manifold embedding
Hyperspectral imaging
Semantic segmentation
Brain cancer detection
Fecha de publicación: 2017
Revistas: IEEE Transactions on Medical Imaging 
Resumen: Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intraoperative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.
URI: http://hdl.handle.net/10553/35490
ISSN: 0278-0062
DOI: 10.1109/TMI.2017.2695523
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